Target Detection and Tracking of moving objects for characterizing landslide displacements from time-lapse terrestrial optical images

Abstract

Terrestrial Optical Photogrammetry (TOP) is a low-cost monitoring technique that is commonly used in change detection studies. For landslide monitoring, image correlation techniques are frequently used to generate Digital Surface Models (DSMs) from stereo-pairs or to measure the 2D surface displacement field from single-view time-lapse sequences. Image correlation allows for detecting spatially continuous displacement fields at a sub-pixel precision. The technique, however, has several limitations for assessing displacement because 1) of its sensitiveness to changes in texture, shape and radiometry in the image pile, 2) of the need of spatially regular sampling grids, and 3) of possible high computation time that can impede the processing of large image datasets.To address these limitations, an alternative and complementary approach based on a Target Detection and Tracking (TDT) algorithm is proposed for a rapid calculation of the displacement of targets in image time series. The TDT code, developed as a MATLAB-based tool, is able to track natural or man-made targets. The precision of the TDT code is assessed using several image time series acquired at the Super-Sauze landslide (Southern French Alps) and compared to ground based measurements. The computed relative accuracy is between 10−3 and 10−4 (5cm at a distance of 115m). Although the TDT approach does not provide spatially continuous information, it provides 1) a quantification of the object displacements at the same order of precision as image correlation (sub-pixel accuracy) and 2) information in regions where image correlation fails because of too large ground displacements. A sensitivity analysis reveals that the major sources of uncertainty are camera movement and/or lens distortion and not the TDT method itself.